Abstract
Reasonable automatic guided vehicle path planning can shorten the transportation time of materials and improve the production efficiency of the intelligent assembly workshop. Ant colony algorithm is a widely used path planning method, however, it suffers from the shortcomings that being easy to fall into local optimum and low search efficiency. To overcome these shortcomings, first, this paper proposes a step optimization method to improve the search efficiency of the ant colony algorithm, and a path simplification method to avoid getting blindly tortuous paths; Second, to overcome the problem that the ant colony algorithm is easy to fall into the local optimum, this paper proposes an adaptive pheromone volatilization coefficient strategy, which uses different pheromone volatilization coefficients at different stages of the search path; third, for the path conflict problem of multiple automatic guided vehicles, this paper proposes a load balancing strategy to avoid it, which is based on the consideration that, path conflicts are caused by excessive concentration of multiple automatic guided vehicles paths. Extensive simulation results demonstrate the feasibility and efficiency of the proposed methods.
Highlights
With the steady progress of the Made in China 2025 and the Industry 4.0 plans, advanced technologies such as digital factories, Industrial Internet of Things, and artificial intelligence have developed rapidly
The ants, which increases the randomness when the ant colony optimization (ACO) algorithm is used to search for the path and prevents the ACO algorithm from falling into the local optimum [33]; if the pheromone volatilization coefficient takes a smaller value, the guiding effect of the pheromone on the ants increases, which is not conducive to the global divergence search path, and the algorithm is easy to fall into local optimum
Aiming at the shortcomings of the low search efficiency of ACO algorithm, this paper proposes a strategy to improve the search step length to improve search efficiency, and use the method of simplifying path to avoid getting blindly tortuous paths
Summary
With the steady progress of the Made in China 2025 and the Industry 4.0 plans, advanced technologies such as digital factories, Industrial Internet of Things, and artificial intelligence have developed rapidly. Planning the optimal path from the starting position to the target position for the AGV that accepts the task is the key to improving the work efficiency and economic benefits of the intelligent assembly workshop [2][3]. The contribution of this paper can be summarized as follows: First, a method is proposed to improve the search efficiency by the ACO algorithm with adaptive search step length to search the path. When multiple AGVs perform tasks together, in view of the inefficiency of the multi-AGV scheduling system caused by path conflicts of multiple AGVs, this paper proposes a method based on load balancing to avoid path conflicts of multiple AGVs. The rest of the paper is organized as follows: Section 2 presents related research on path planning.
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